{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pyMRST_water_incomp.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPgBgBCpzVX+C3OYNs7qsCm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/pyMRST/blob/main/notebooks/pyMRST_water_incomp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXXDnZOgYG_Q"
      },
      "source": [
        "# Simulation of Incompressible Water Reservoir with PyMRST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3_fpJXZ8d0G",
        "outputId": "7804a93d-f23b-4808-eeb0-50ce88c9c629"
      },
      "source": [
        "# Clone pyMRST\n",
        "!git clone https://github.com/yohanesnuwara/pyMRST"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pyMRST'...\n",
            "remote: Enumerating objects: 273, done.\u001b[K\n",
            "remote: Counting objects: 100% (141/141), done.\u001b[K\n",
            "remote: Compressing objects: 100% (139/139), done.\u001b[K\n",
            "remote: Total 273 (delta 70), reused 0 (delta 0), pack-reused 132\u001b[K\n",
            "Receiving objects: 100% (273/273), 784.81 KiB | 3.09 MiB/s, done.\n",
            "Resolving deltas: 100% (116/116), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqfpKzMp8zZk"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Add directory where you install PyMRST\n",
        "import sys\n",
        "sys.path.append(\"/content/pyMRST\") \n",
        "\n",
        "import pymrst\n",
        "from pymrst_units import *"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OIu4smY9jqx"
      },
      "source": [
        "# Setup PyMRST (Takes about 1 minute)\n",
        "pymrst.setup()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDfyklaGzah1"
      },
      "source": [
        "from pymrst_units import *\n",
        "\n",
        "# Geometry\n",
        "nx, ny, nz = 30, 20, 10\n",
        "lx, ly, lz = 30, 20, 10\n",
        "\n",
        "# Reservoir property                                         \n",
        "poro = dict({\"type\": \"heterogeneous\", \n",
        "             \"field\": \"gaussian\", \n",
        "             \"min\": 0.2, \"max\": 0.4, \"std\": 2.5})\n",
        "\n",
        "k = dict({\"type\": \"heterogeneous\", \n",
        "          \"field\": \"kozeny\"})\n",
        "\n",
        "ntg = dict({\"type\": \"heterogeneous\", \n",
        "            \"field\": \"lognorm\", \n",
        "            \"min\": 0.4, \"max\": 0.6})\n",
        "\n",
        "rock = dict({\"c\": 1e-6/barsa(), # Rock compressibility at ref pressure, 1/bar to 1/Pa\n",
        "             \"p_r\": 200*barsa()}) # Reference pressure, bar to Pa             \n",
        "\n",
        "# Fluid property: 1-phase water\n",
        "fluid = dict({\"type\": \"water\", \n",
        "              \"mu\": 1*centi()*poise(), # cp to Pa.s \n",
        "              \"rho\": 1014})\n",
        "\n",
        "# Boundary \n",
        "bc_front = dict({\"type\": \"fluxside\", \"value\": 50*stb()/day()}) # bbl/d to m3/s\n",
        "bc_back = dict({\"type\": \"fluxside\", \"value\": 0})\n",
        "bc_left = dict({\"type\": \"fluxside\", \"value\": 100/day()}) # m3/d to m3/s\n",
        "bc_right = dict({\"type\": \"pside\", \"value\": 100*barsa()}) # bar to Pa\n",
        "\n",
        "# Well\n",
        "cell_loc1 = np.arange(10+60, nx*ny*nz, nx*ny)\n",
        "cell_loc2 = np.arange(nx, nx*ny, ny)\n",
        "\n",
        "# well = dict({\"cell_loc\": [cell_loc1, cell_loc2], \n",
        "#              \"type\": [\"rate\", \"bhp\"], \n",
        "#              \"value\": [(-10*stb()/day()), (110*barsa())], # bbl/d to m3/s, bar to Pa \n",
        "#              \"radius\": [.1, .1],\n",
        "#              \"skin\": [0, 0], \n",
        "#              \"direction\": [None, \"y\"],\n",
        "#              \"phase\": [[1,0], [0,1]]}) # Phase  \n",
        "\n",
        "# well = dict({\"cell_loc\": [cell_loc1], \n",
        "#              \"type\": [\"rate\"], \n",
        "#              \"value\": [(-10*stb()/day())], # bbl/d to m3/s, bar to Pa \n",
        "#              \"radius\": [.1],\n",
        "#              \"skin\": [0], \n",
        "#              \"direction\": [None]})  \n",
        "\n",
        "well = dict({\"cell_loc\": [cell_loc1], \n",
        "             \"type\": [\"bhp\"], \n",
        "             \"value\": [100*barsa()], # bbl/d to m3/s, bar to Pa \n",
        "             \"radius\": [.1],\n",
        "             \"skin\": [0], \n",
        "             \"direction\": [None]})  \n",
        "\n",
        "# Execute function\n",
        "pymrst.write_input(nx, ny, nz, lx, ly, lz, poro, k, rock, fluid, well, \n",
        "                   bc_front, bc_back, bc_left, bc_right)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ryzJRhlhV-G4",
        "outputId": "52120b7f-271b-417c-b22f-f3a4fc0e1611"
      },
      "source": [
        "# Execute simulator\n",
        "# Simulator results are added in new directory \"result_water_1phase\"\n",
        "pymrst.water_1phase()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "warning: function /content/mrst-core/utils/mex/md5sum.m shadows a core library function\n",
            "Computing one-sided transmissibilities...\tElapsed time is 0.0113828 seconds.\n",
            "warning: Inconsistent Number of Phases.  Using 1 Phase (=min([3, 1, 1])).\n",
            "ans = 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "EhpnSQBU1XC0",
        "outputId": "6871e23c-92a7-4728-8d09-01a8a3fb3d74"
      },
      "source": [
        "# Plot pressure, porosity, and permeability\n",
        "plt.figure(figsize=(12,8))\n",
        "\n",
        "# Inputs for plotting\n",
        "directory = \"/content/result_water_1phase\"\n",
        "filename = [\"pressure.mat\", \"poro.mat\", \"perm.mat\"]\n",
        "plane, position = \"xy\", 0\n",
        "\n",
        "# Plot\n",
        "titles = [\"Pressure\", \"Porosity\", \"Permeability\"]\n",
        "\n",
        "for i in range(3):\n",
        "  plt.subplot(2,2,i+1)\n",
        "  plt.title(\"{} on {} Plane at Slice {}\".format(titles[i], plane, position),\n",
        "          size=15, pad=12) \n",
        "\n",
        "  # Get cell data\n",
        "  cube = pymrst.getCellData(directory, filename[i], dimension=(nx,ny,nz))\n",
        "\n",
        "  # Convert units of pressure and permeability\n",
        "  if i==0:\n",
        "    # Convert metric (Pa) to field (psia)\n",
        "    cube = cube/barsa()  \n",
        "  if i==2:\n",
        "    # Convert metric (Pa.s) to field (md)\n",
        "    cube = cube/(milli()*darcy()) \n",
        "\n",
        "  # Plot cell data\n",
        "  pymrst.plotCellData(cube, plane, position, cmap=\"plasma\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}